# encoding: utf-8
# https://www.bilibili.com/video/BV1Ja4y1B7zC/?spm_id_from=333.788&vd_source=2be60dc3c045dc4bd9130d2ec456bcc9
# 报错了。。。


import torch

import torch.nn as nn


class SelfAttention(torch.nn.Module):

    def __init__(self, embed_size, heads):
        super(SelfAttention, self).__init__()
        self.embed_size = embed_size
        self.heads = heads
        self.head_dim = embed_size // heads

        self.values = nn.Linear(embed_size, embed_size)
        self.keys = nn.Linear(embed_size, embed_size)
        self.quires = nn.Linear(embed_size, embed_size)

        self.fc_out = nn.Linear(embed_size, embed_size)

    def forward(self, values, keys, query, mask):
        N = query.shape[0]

        value_len, key_len, query_len = values.shape[1], keys.shape[1], query.shape[1]

        values = self.values(values)
        keys = self.values(keys)
        values = self.values(values)
        queries = self.values(query)

        energy = torch.einsum('nqhd,nkhd->nhqk', [queries, keys])
        if mask is not None:
            energy = energy.mask_fill(mask == 0, float('-1e20'))

        attention = torch.softmax(energy / (self.embed_size ** (1 / 2)), dim=3)

        out = torch.einsum('nhql,nlhd->nqhd', [attention, values]).reshape(N, query_len, self.heads * self.head_dim)

        return out


class TransformerBlock(nn.Module):
    def __init__(self, embed_size, heads, dropout, forward_expansion):
        super(TransformerBlock, self).__init__()
        self.attention = SelfAttention(embed_size, heads)
        self.norm1 = nn.LayerNorm(embed_size)
        self.norm2 = nn.LayerNorm(embed_size)

        self.feed_forward = nn.Sequential(
            nn.Linear(embed_size, forward_expansion * embed_size),
            nn.ReLU(),
            nn.Linear(forward_expansion * embed_size, embed_size)
        )
        self.dropout = nn.Dropout(dropout)

    def forward(self, value, key, query, mask):
        attention = self.attention(value, key, query, mask)
        x = self.dropout(self.norm1(attention + query))
        forward = self.feed_forward(x)
        out = self.dropout(self.norm2(forward + x))

        return out


class Encoder(nn.Module):
    def __init__(self, src_vocab_size, embed_size, num_layers, heads, device, forward_expansion, dropout, max_length):
        super(Encoder, self).__init__()
        self.embed_size = embed_size
        self.device = device
        self.word_embedding = nn.Embedding(src_vocab_size, embed_size)  # a * b
        self.position_embedding = nn.Embedding(max_length, embed_size)  # max_length * embed_size

        self.layers = nn.ModuleList(
            [TransformerBlock(embed_size, heads, dropout, forward_expansion) for _ in range(num_layers)]
        )
        self.dropout = nn.Dropout(dropout)

    def forward(self, x, mask):
        N, seq_length = x.shape
        positions = torch.arange(0, seq_length).expand(N, seq_length).to(self.device)
        out = self.dropout(
            (self.word_embedding(x) + self.position_embedding(positions))
        )

        for layer in self.layers:
            out = layer(out, out, out, mask)

        return out


class DecoderBlock(nn.Module):
    def __init__(self, embed_size, heads, forward_expansion, dropout, device):
        super(DecoderBlock, self).__init__()
        self.norm = nn.LayerNorm(embed_size)
        self.attention = SelfAttention(embed_size, heads)

        self.transformer_block = TransformerBlock(embed_size, heads, dropout, forward_expansion)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x, value, key, src_mask, trg_mask):
        attention = self.attention(x, x, x, trg_mask)
        query = self.dropout(self.norm(attention + x))
        out = self.transformer_block(value, key, query, src_mask)

        return out


class Decoder(nn.Module):
    def __init__(self, trg_vocab_size, embed_size, num_layers, heads, forward_expansion, dropout, device, max_length):
        super(Decoder, self).__init__()
        self.device = device
        self.word_embedding = nn.Embedding(trg_vocab_size, embed_size)
        self.position_embedding = nn.Embedding(max_length, embed_size)

        self.layers = nn.ModuleList(
            [DecoderBlock(embed_size, heads, forward_expansion, dropout, device)
             for _ in range(num_layers)
             ]
        )

        self.fc_out = nn.Linear(embed_size, trg_vocab_size)

    def forward(self, x, enc_out, src_mask, trg_mask):
        N, seq_length = x.shape
        positions = torch.arange(0, seq_length).expand(N, seq_length).to(self.device)
        x = self.dropout((self.word_embedding(x) + self.position_embedding(positions)))

        for layer in self.layers:
            x = layer(x, enc_out, enc_out, src_mask, trg_mask)

        out = self.fc_out(x)

        return out


class Transformer(nn.Module):
    def __init__(self, src_vocab_size, trg_vocab_size, src_pad_idx, trg_pad_idx, embed_size=512, num_layers=6,
                 forward_expansion=4, heads=8,
                 dropout=0, device="cpu", max_length=100):
        super(Transformer, self).__init__()
        self.encoder = Encoder(src_vocab_size, embed_size, num_layers, heads, device, forward_expansion, dropout,
                               max_length)
        self.decoder = Decoder(trg_vocab_size, embed_size, num_layers, heads, forward_expansion, dropout, device,
                               max_length)
        self.src_pad_idx = src_pad_idx
        self.trg_pad_idx = trg_pad_idx
        self.device = device

    def make_src_mask(self, src):
        src_mask = (src != self.src_pad_idx).unsqueeze(1).unsqueeze(2)
        return src_mask.to(self.device)

    def make_trg_mask(self, trg):
        N, trg_len = trg.shape
        trg_mask = torch.tril(torch.ones((trg_len, trg_len))).expand(
            N, 1, trg_len, trg_len
        )

        return trg_mask.to(self.device)

    def forward(self, src, trg):
        src_mask = self.make_src_mask(src)
        trg_mask = self.make_trg_mask(trg)
        enc_src = self.encoder(src, src_mask)
        out = self.decoder(trg, enc_src, src_mask, trg_mask)

        return out


if __name__ == '__main__':
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    print(device)

    x = torch.tensor([[1, 5, 6, 4, 3, 9, 5, 2, 0], [1, 8, 7, 3, 4, 5, 6, 7, 2]]).to(device)

    trg = torch.tensor([[1, 7, 4, 3, 5, 9, 2, 0], [1, 5, 6, 2, 4, 7, 6, 2]]).to(device)

    src_pad_idx = 0
    trg_pad_idx = 0
    src_vocab_size = 10
    trg_vocab_size = 10

    model = Transformer(src_vocab_size, trg_vocab_size, src_pad_idx, trg_pad_idx, device=device).to(device)
    out = model(x, trg[:, :-1])
    print(out.shape)

